1. Field of the Invention
The present invention relates generally to computerized object recognition and, more particularly, to object recognition and classification using a three-dimensional (3D) system.
2. Description of the Related Art
Computerized object recognition is the process of finding or identifying an object in an image or video. Recognizing an object can include the process of classifying objects belonging to distinct classes. Object classifying using computer vision can be applied to, among other things, automated production processes, security, and automotive applications.
The majority of object recognition technologies today use camera images as the input or another suitable two-dimensional sensor. Each image serves as an input to an object recognition algorithm, such as a neural network or another machine learning system. The image is usually fed into the algorithm as a collection of features, e.g., pixel intensities. The temporal order of such features is meaningless in the context of a single image. More importantly, the number of features can be very large, making the task of object recognition computationally very demanding. Most object recognition technologies inputting 3-D images project the collected data into a two-dimensional space and then track features as just described.
Object recognition is known to be especially difficult if the object position and orientation is not constrained (i.e., the object may appear from an arbitrary viewing angle). In order to recognize and classify objects with a high degree of reliability, computer vision systems need to account for this variance. Reliable rotational invariant recognition of objects has remained an unsolved problem.